Introducing Microsoft SQL Server 2019 Big Data Clusters

SQL Server 2019 big data clusters make it easier for big data sets to be joined to the dimensional data typically stored in the enterprise relational database, enabling people and apps that use SQL Server to query big data more easily. The value of the big data greatly increases when it is not just in the hands of the data scientists and big data engineers but is also included in reports, dashboards, and applications. At the same time, the data scientists can continue to use big data ecosystem tools while also utilizing easy, real-time access to the high-value data in SQL Server because it is all part of one integrated, complete system.

Starting in SQL Server 2017 with support for Linux and containers, Microsoft has been on a journey of platform and operating system choice. With SQL Server 2019 preview, we are making it easier to adopt SQL Server in containers by enabling new HA scenarios and adding supported Red Hat Enterprise Linux container images. Today we are happy to announce the availability of SQL Server 2019 preview Linux-based container images on Microsoft Container Registry, Red Hat-Certified Container Images, and the SQL Server operator for Kubernetes, which makes it easy to deploy an Availability Group.

Microsoft Azure Data Studio

Azure Data Studio is a new cross-platform desktop environment for data professionals using the family of on-premises and cloud data platforms on Windows, MacOS, and Linux. Previously released under the preview name SQL Operations Studio, Azure Data Studio offers a modern editor experience with lightning fast IntelliSense, code snippets, source control integration, and an integrated terminal. It is engineered with the data platform user in mind, with built-in charting of query resultsets and customizable dashboards.

The Microsoft Data Science Virtual Machine (DSVM) is a powerful data science development environment that enables you to perform various data exploration and modeling tasks. The environment comes already built and bundled with several popular data analytics tools that make it easy to get started quickly with your analysis for On-premises, Cloud or hybrid deployments. The DSVM works closely with many Azure services and is able to read and process data that is already stored on Azure, in Azure SQL Data Warehouse, Azure Data Lake, Azure Storage, or in Azure Cosmos DB. It can also leverage other analytics tools such as Azure Machine Learning and Azure Data Factory.

Tools for ML model operationalization as web services in the cloud, using Azure ML or Microsoft R Server.

Creating the Azure DSVM with Windows Server 2016

I use this Azure DSVM for testing and I will enable Auto-Shutdown Scheduler
(to save money)

The following software is by default installed in the Azure DSVM :

My Azure Data Science Virtual Machine in the Cloud 😉

From here you can configure your Data Management Gateway.

Microsoft Data Management Gateway connects on-premises data sources to cloud services for consumption. With Microsoft cloud services, such as Power BI for Office 365 and Azure Data Factory you get benefits including fast deployment, low maintenance cost, and flexible billing model while keeping your enterprise data on-premises. With Data Management Gateway, you can connect on-premises data to cloud services in a secure and managed way, to respond more quickly to changing business needs with a flexible, hybrid cloud platform. You can benefit from Microsoft cloud services while you keep your business running with the on-premises data.